This Friday Fave is more for utility than pleasure.
Unfortunately, I have been working to determine why my views and query layers perform so much worse than directly accessing my feature class.
My Googling led me to Geodatabase Geek, by Trevor Hart, Eagle Technology Group Ltd. Trevor has some real good information about Geodatabases and also gave a good lightening talk on Usage Reporting on ArcGIS 10.1 for Server at the 2013 ESRI International Developer’s Conference.
One tool he pointed out was Mxdperfstat for benchmarking the performance of your MXD. Trevor used it to compare the performance of a Feature Class vs Query Layer vs Spatial View. While the official version is available for ArcGIS 9.3 through 10.2, I do want to point out Hussein Nasser’s 10.1 version which he put out before the official 10.1 version came out (it’s not really a version, more of a work-around but I like his ingenuity).
My results were significantly different on our 10.0 database server, the spatial view I was testing was much slower. The query for both the spatial view and query layer was simply “Select * from featureclass”
So not sure what to make of the performance yet, I’ve got a spatial index made so not sure what else I can try.
This Friday Fave is a little bit different.
My interest in geospatial technologies (although we just called it GIS back then) largely because I wanted to measure my running routes more accurately and efficiently than the paper map & scrap of paper method I was using in the early 90s. When I was introduced to GIS, I knew what I was going to use it for.
Now that GPS technology is ubiquitous–I’m currently using four different GPS devices, at the same time, on my bike rides–I seldom have to use a map to measure my routes. I may still use MapMyRun.com to plan a route ahead of time if I’m running in a new area or trying to plan a loop of a certain distance but GPS has really made it so simple to just go out and run.
While there are several GPS options available, I have used Garmin ever since their 405 Forerunner came out. This was their first watch that didn’t get confused for a Timex-Sinclair 1000 strapped to your wrist.
Garmin’s watches upload their data to Gamin Connect, which works fine, but for a project I recently started, I wanted to down load all my data which Garmin Connect does not make easy. I had over 1,000 data logs and downloading them individually was not going to happen.
A little Googling led me to tapiriik.com, which allows you to share data amongst several different online services that endurance athletes might use including Runkeeper, Strava, Garmin Connect, SportTracks, DropBox.com, and Training Peaks.
You can use Tapiriik for free (you just need to visit their website to start synchronization) or for $2 per year they will automatically synchronize data between your accounts. You just provide your account information for whichever sites you want to synchronize and either visit their site or pay $2 and it will automatically share data between your accounts.
I linked to my Garmin Connect and Dropbox accounts and tapiriik and after some chugging, I had .GPX files for all of my data.
It was easy and didn’t take very long. Definitely met my needs–I haven’t shared data between other services but I do use the desktop version of SportTracks and considered using their web version of the software but didn’t know how I would upload all my data. Now I know.
This product definitely saved me a bunch of time. The one thing I wonder, though, is what does “Tapiriik” means?
I admit, I love picking up freebie maps. Whether it is from the front desk of a hotel or from the bicycle shop, there is a certain appeal to seeing what people put on maps. I have maps organic orchards, breweries, Minnesota authors, rails to trails, zoos, fictional places, race maps, and a variety of other things that someone felt the need to cartographize.
This app allows you to take a picture (or use an image on your device) and georeference it. You can then view your location on the map. I’ve tried it with a few maps and have been happy with the results.
Similar to georeferencing in a desktop application, you select points on the map and then the same points on a control. You need at lest two or three points. After doing it a couple of times, the process becomes pretty easy and it just takes a minute or two from taking the picture to viewing your current location on the map.
An example use case scenario for this app is you are lugging your family around at an overwhelming large amusement park and never quite sure where you are. You can take a picture of the map you picked up at the entrance, georeference it, and your phone will then show exactly where you are on that map and where the nearest loo is.
The biggest limitation I’ve seen so far is that there is a limit of 3-5 megapixel size limit on the image. Apparently this is an android limitation on how much memory an app can consume. But if you adjust your camera settings not to exceed this, you should be good.
So far, I’m enjoying this app. The author, Marko Teittinen (a good Finn name), has made the source code open source so I look forward to digging into it in more detail.
Obviously Cartographers belong in the same category as other superheroes like Superman, Batman, and Spiderman and we finally have a our own comic book to prove it.
Cartozia Tales is a collaborative effort of nine indy artists with two guest artist each issue.
They have an interesting plan, they’ve split the world into nine regions (what’s the name for the ninth of an area, nona-rant?) and the artist will tell a story from a different region each issue. They may build off the region’s story from the previous issue, continue on the story they told the previous issue, and start something fresh.
After being funded by a KickStarter campaign, they’ve shipped three issues already and have at least seven more promised. Think the project definitely deserve’s a look.
When I first found out about GIS, the first application that came to mind was using it to map my running routes. At that time, I was using paper maps and scraps of paper to measure how far I was running each day. GIS obviously offered a better method.
Almost twenty years later, GPS has become so common place that I think we have four or five devices in our household that have GPS capabilities and measuring my runs has become ridiculously simple.
Given that background, you can understand why I love projects like this project by Nikita Barsukov, who compiled publicly available data from Endomondo and created maps of public running workout tracks for a variety of European Cities, including this sample from Helsinki.
I’ve got about six years of running data of my own that I should use to generate a personal running route map but would be a fun project.